Papers by Jean-Flavien Bussotti
Refining Attention for Explainable and Noise-Robust Fact-Checking with Transformers (2025.emnlp-main)
Copied to clipboard
| Challenge: | Conventional transformer-based models falter due to noise sensitivity and lack explainability . ATTUN is a transformer architecture designed to enhance model transparency and resilience to noise. |
| Approach: | They propose a transformer architecture that enhances model transparency and resilience to noise . ATTUN is a module that directly modifies attention weights . they validated their approach using fact-checking datasets based on their results . |
| Outcome: | The proposed model improves predictions and identify relevant sections of input data. |
Unknown Claims: Generation of Fact-Checking Training Examples from Unstructured and Structured Data (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for fact-checking are labor-intensive and time-consuming. |
| Approach: | They propose a framework that generates training instances for FC systems automatically using textual and tabular content. |
| Outcome: | The proposed framework generates training instances for FC systems using textual and tabular content. |